Regional campaing’s results (PhD Chapter 1)

This series of files compile all analyses done during Chapter 1 for the regional campaign (2016):

All analyses have been done with PRIMER-e 6 and R 3.6.0.

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Caracteristics of each campaign

2014 2016 2017
Sampling date August-September June to August July
Criteria for perturbation Potentially impacted if close to the city or industries, References outside the bay Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria
Regions considered BSI BSI, CPC, BDA, MR BSI, MR
Number of sampled stations 40 (20 HI, 20 R) 78 (26 BSI, 19 CPC, 18 BDA, 15 MR) 126 (111 BSI, 15 MR)
Parameters sampled Organic matter yes yes yes
Photosynthetic pigments no yes yes
Sediment grain-size yes yes yes
Heavy-metals yes yes (for a limited number of stations) no (interpolated based on 2014 and 2016 values)
Benthic communities Compartment targeted Macro-infauna Macro-infauna Macro-infauna
Sieved used 500 µm 1 mm 500 µm and 1 mm
Conservation technique Formaldehyle Formaldehyle Formaldehyle
Others N.A. N.A. N.A.

We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.

Selected variables for the analyses:

Abundances of Mesodesma arctatum (Marc) and Cistenides granulata (Cgra) were also considered (see IndVal and SIMPER results).

As data is missing for metal concentrations outside BSI, two Designs have been used:

Statistics for each variable considered:

BSI, CPC, BDA, PR (without heavy metals)
  Mean SD SE Median Min Max 95% CI
depth 21.131 17.504 1.982 16.100 1.000 65.900 3.884
om 0.689 0.730 0.083 0.387 0.168 3.863 0.162
gravel 0.049 0.122 0.014 0.000 0.000 0.809 0.027
sand 0.707 0.348 0.039 0.873 0.000 1.001 0.077
silt 0.200 0.285 0.032 0.031 0.000 0.942 0.063
clay 0.043 0.098 0.011 0.006 0.000 0.497 0.022
S 4.795 2.969 0.336 4.000 0.000 13.000 0.659
N 19.846 25.867 2.929 12.000 0.000 142.000 5.740
H 1.080 0.610 0.069 1.069 0.000 2.307 0.135
J 0.724 0.292 0.033 0.838 0.000 1.000 0.065
BSI (heavy metals)
  Mean SD SE Median Min Max 95% CI
arsenic 4.188 4.582 0.899 2.750 0.800 21.300 1.761
cadmium 0.152 0.047 0.009 0.145 0.080 0.270 0.018
chromium 49.385 23.565 4.621 42.500 17.000 111.000 9.058
copper 9.373 7.593 1.489 6.450 2.400 28.800 2.919
iron 45356.633 16635.601 3262.510 42304.650 21938.100 85408.600 6394.402
manganese 776.662 348.066 68.261 711.450 318.400 1657.100 133.790
mercury 0.023 0.017 0.003 0.019 0.007 0.091 0.006
lead 5.381 2.902 0.569 4.750 1.900 12.200 1.115
zinc 58.262 29.368 5.760 46.800 26.900 141.400 11.289

1. Data manipulation

For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices.

1.1. Identification of outliers

To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.

Design 1

Based on Cook’s Distance, we identified stations 80, 96, 107 and 126 as general outliers. They have been deleted for the following analyses of Design 1.

Design 2

Based on Cook’s Distance, we identified stations 108 and 110 as general outliers. They have been deleted for the following analyses of Design 2.

1.2. Correlations between parameters

Correlations have been calculated with Spearman’s rank coefficient.

Design 1

Correlation coefficients between habitat parameters (Design 1)
  om gravel sand silt clay
om 1 -0.045 -0.823 0.737 0.731
gravel -0.045 1 -0.186 -0.347 -0.296
sand -0.823 -0.186 1 -0.793 -0.799
silt 0.737 -0.347 -0.793 1 0.972
clay 0.731 -0.296 -0.799 0.972 1

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 1:

  • silt and clay (clay deleted)

We decided to keep sand, even if it is correlated with om, to stay consistant with the local campaign.

Design 2

Correlation coefficients between heavy metals concentrations (Design 2)
  arsenic cadmium chromium copper iron manganese mercury lead zinc
arsenic 1 0.476 0.729 0.835 0.772 0.389 0.626 0.819 0.888
cadmium 0.476 1 0.731 0.421 0.717 0.825 0.108 0.675 0.66
chromium 0.729 0.731 1 0.665 0.799 0.753 0.439 0.84 0.852
copper 0.835 0.421 0.665 1 0.56 0.296 0.518 0.805 0.873
iron 0.772 0.717 0.799 0.56 1 0.728 0.395 0.71 0.807
manganese 0.389 0.825 0.753 0.296 0.728 1 0.074 0.546 0.565
mercury 0.626 0.108 0.439 0.518 0.395 0.074 1 0.61 0.505
lead 0.819 0.675 0.84 0.805 0.71 0.546 0.61 1 0.881
zinc 0.888 0.66 0.852 0.873 0.807 0.565 0.505 0.881 1

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 2:

  • cadmium and manganese (manganese deleted)
  • copper, lead and zinc (copper and zinc deleted)

We decided to keep arsenic, even though it is correlated with the copper/lead/zinc group, to stay consistant with the local campaign.

2. Permutational Analyses of Covariance

Results of univariate PermANCOVAs on parameters and multivariate PermANCOVA on the whole benthic community with depth as covariate are presented in the table below. Variables were normalized and abundances were (log+1) transformed.

Variable Condition Region(Co) Depth Significative groups of similar regions (p > 0.05)
om S S {CPC BDA MR}
gravel All regions in the same group
sand S All regions in the same group
silt S S {BSI CPC BDA}, {BDA MR}
clay {BSI BDA MR}, {CPC MR}
S (1 mm) S {BSI CPC MR}, {CPC BDA MR}
N (1 mm) All regions in the same group
H (1 mm) S S {CPC BDA MR}, {BSI MR}
J (1 mm) {BSI CPC MR}, {CPC BDA MR}
ALL SPECIES (1 mm) S S

3. Similarity and characteristic species

Let’s have a look at the \(\Beta\) diversity within our conditions and sites.

Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion). Abundances were (log+1) transformed.

Condition or Site Mean SE
HI 63.8 0.82
R 61.9 1.14
BSI 62.7 1.11
CPC 57.8 2.33
BDA 61.1 1.93
MR 58.2 2.11

No significative relationships were found for either factor, but BSI was detected different of CPC and MR.

Here are the values of the mean Bray-Curtis dissimilarity for each group.

Mean within-group dissimilarity for each condition or region (Bray-Curtis, %)
  HI R BSI CPC BDA MR
Mean BC 0.915 0.877 0.898 0.87 0.882 0.835

The following analyses allowed to detect species as characteristic of each condition. We used results from PRIMER to justify further their choice.

##                       cluster indicator_value probability
## cistenides_granulata        1          0.3055       0.012
## ennucula_tenuis             1          0.2222       0.001
## macoma_calcarea             1          0.2222       0.004
## eudorellopsis_integra       1          0.1556       0.038
## mesodesma_arctatum          2          0.2357       0.003
## harmothoe_imbricata         2          0.1981       0.005
## glycera_alba                2          0.1212       0.029
## psammonyx_nobilis           2          0.1212       0.036
## 
## Sum of probabilities                 =  49.883 
## 
## Sum of Indicator Values              =  6.03 
## 
## Sum of Significant Indicator Values  =  1.58 
## 
## Number of Significant Indicators     =  8 
## 
## Significant Indicator Distribution
## 
## 1 2 
## 4 4
SIMPER results (mean between-group dissimilarity: 0.926 )
  average sd ratio ava avb cumsum
echinarachnius_parma 0.0963 0.134 0.717 0.689 0.417 0.104
mesodesma_arctatum 0.0685 0.127 0.538 0.605 0.095 0.178
cistenides_granulata 0.0603 0.0928 0.649 0.176 0.57 0.243
nephtys_caeca 0.0416 0.0549 0.757 0.359 0.22 0.288
strongylocentrotus_sp 0.0415 0.0747 0.556 0.27 0.237 0.333
limecola_balthica 0.0318 0.0571 0.557 0.234 0.208 0.367
scoloplos_armiger 0.0285 0.0641 0.444 0.14 0.245 0.398
macoma_calcarea 0.0262 0.0559 0.469 0 0.298 0.426
protomedeia_grandimana 0.0253 0.0561 0.45 0.183 0.23 0.453
harmothoe_imbricata 0.0252 0.0574 0.438 0.217 0.0154 0.48
amphipholis_squamata 0.0248 0.0607 0.408 0.042 0.254 0.507
ennucula_tenuis 0.0221 0.0455 0.485 0 0.31 0.531
thyasira_sp 0.0204 0.0482 0.423 0.021 0.284 0.553
psammonyx_nobilis 0.0186 0.0583 0.319 0.185 0 0.573
mya_arenaria 0.0168 0.0335 0.503 0.063 0.16 0.591
ciliatocardium_ciliatum 0.0136 0.0442 0.309 0.0908 0.0732 0.606
goniada_maculata 0.0133 0.0347 0.383 0.021 0.166 0.62
glycera_dibranchiata 0.0128 0.0422 0.304 0.021 0.077 0.634
glycera_alba 0.0126 0.0403 0.312 0.172 0 0.648
astarte_undata 0.0123 0.0391 0.316 0.142 0.0154 0.661
ameritella_agilis 0.0112 0.0481 0.232 0 0.125 0.673
astarte_subaequilatera 0.0107 0.0367 0.292 0.134 0 0.685
eudorellopsis_integra 0.00989 0.0264 0.374 0 0.162 0.695
pygospio_elegans 0.0097 0.0444 0.219 0.137 0.0154 0.706
nucula_proxima 0.00949 0.0342 0.277 0 0.107 0.716
ophelia_limacina 0.00946 0.0293 0.322 0.042 0.0552 0.726
diastylis_sculpta 0.00939 0.0399 0.235 0.0488 0.0308 0.736
ampharetidae_spp 0.00933 0.0275 0.34 0.0753 0.0512 0.746
polynoidae_spp 0.00918 0.0227 0.404 0.021 0.122 0.756
yoldia_myalis 0.00889 0.028 0.317 0.0543 0.0462 0.766
ampeliscidae_spp 0.00882 0.0251 0.352 0.063 0.0488 0.776
nephtys_bucera 0.00881 0.0252 0.35 0.063 0.0308 0.785
pontoporeia_femorata 0.00839 0.0396 0.212 0 0.126 0.794
maldanidae_spp 0.00809 0.0269 0.301 0.0908 0.0308 0.803
bipalponephtys_neotena 0.008 0.0362 0.221 0 0.101 0.811
testudinalia_testudinalis 0.00751 0.0284 0.265 0.08 0.0244 0.82
pagurus_pubescens 0.0075 0.0228 0.329 0.0753 0.0154 0.828
ampharete_oculata 0.00712 0.0433 0.165 0.0666 0 0.835
nephtys_ciliata 0.00684 0.0264 0.259 0 0.0924 0.843
phyllodoce_mucosa 0.00615 0.0236 0.261 0 0.101 0.849
phyllodocidae_spp 0.00607 0.0208 0.293 0.021 0.0462 0.856
phoxocephalus_holbolli 0.00594 0.0322 0.185 0 0.079 0.862
harpinia_propinqua 0.00537 0.025 0.215 0.0753 0.0154 0.868
quasimelita_formosa 0.00464 0.0188 0.247 0 0.0706 0.873
nephtyidae_spp 0.00442 0.0144 0.308 0.021 0.0552 0.878
hiatella_arctica 0.00416 0.0154 0.271 0.021 0.0398 0.882
platyhelminthes 0.0041 0.016 0.256 0 0.0462 0.887
lacuna_vincta 0.00409 0.0228 0.18 0 0.0398 0.891
arrhoges_occidentalis 0.00406 0.0172 0.237 0.0543 0 0.896
cancer_irroratus 0.00396 0.0141 0.28 0.042 0.0154 0.9

4. Univariate regressions

We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses.

4.1. Simple regressions

These analyses have been do to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article (see below).

Design 1

Adjusted R-squared of simple regressions for Design 1
  om gravel sand silt
S 0.09191 0.01477 0.05878 0.1146
N 0.01404 0.002513 0.03299 0.03504
H 0.0883 -0.01004 0.05319 0.0994
J -0.0001471 0.000586 0.01035 0.009213
p-values of simple regressions for Design 1
  om gravel sand silt
S 0.004997 0.1522 0.02111 0.001855
N 0.1576 0.2802 0.06578 0.06001
H 0.005847 0.602 0.02694 0.003605
J 0.3233 0.3106 0.1884 0.1992

Design 2

Adjusted R-squared of simple regressions for Design 2
  arsenic cadmium chromium iron mercury lead
S -0.02448 -0.04543 -0.03355 -0.04355 -0.04506 0.0487
N -0.007642 -0.0415 -0.03362 -0.04259 -0.04478 0.02349
H -0.02186 -0.03955 -0.008538 -0.02448 -0.0236 0.08028
J -0.04475 -0.02601 -0.02849 -0.02868 -0.02868 -0.04439
p-values of simple regressions for Design 2
  arsenic cadmium chromium iron mercury lead
S 0.5091 0.9838 0.6196 0.8432 0.9284 0.1542
N 0.3734 0.7753 0.6207 0.8081 0.9061 0.2258
H 0.4835 0.7271 0.3792 0.5091 0.5003 0.09687
J 0.9043 0.5252 0.5531 0.5554 0.5553 0.8824

Furthermore, depth has been shown important for several parameters in the ANCOVAs, so here are the corresponding scatterplots.

4.2. Multiple regressions

This section presents analyses done (i) to determine which model (Design 1, Design 2) decribes the best the parameters and (ii) which variables are the most important to explain the parameters.

4.2.1. Best model selection

This step was not used here as both models were needed.

4.2.2. Significative variables selection

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:

  • for the model of Design 1
Variable (or combination) S N H J
om
gravel - + +
sand + - + +
silt/clay + - + +
Adjusted \(R^{2}\) 0.13 0.06 0.15 0.04
  • for the model of Design 2
Variable (or combination) S N H J
arsenic
cadmium/manganese
chromium - - -
iron
mercury -
lead/copper/zinc + + +
Adjusted \(R^{2}\) 0.27 0.12 0.18 0

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Design 1

Species richness
## FULL MODEL
## Adjusted R2 is: 0.11
Fitting linear model: S ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.002 4.832 -0.2074 0.8363
om 0.3518 0.7513 0.4683 0.6411
gravel 2.191 5.784 0.3788 0.706
sand 5.129 4.869 1.053 0.2959
silt 8.536 6.459 1.321 0.1907
Variance Inflation Factors
  om gravel sand silt
VIF 1.83 2.15 5.34 5.96 
## REDUCED MODEL
## Adjusted R2 is: 0.13
Fitting linear model: S ~ sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8442 2.242 0.3765 0.7076
sand 3.406 2.349 1.45 0.1514
silt 7.234 2.793 2.59 0.01163 *
Variance Inflation Factors
  sand silt
VIF 2.6 2.6 
## RMSE for the full model: 2.674811 
## RMSE for the reduced model: 2.605987

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.05
Fitting linear model: N ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 108.4 47.04 2.304 0.02423 *
om 3.953 7.314 0.5405 0.5906
gravel -116.2 56.31 -2.063 0.04287 *
sand -92.11 47.4 -1.943 0.05606
silt -97.86 62.88 -1.556 0.1242
Variance Inflation Factors
  om gravel sand silt
VIF 1.83 2.15 5.34 5.96 
## REDUCED MODEL
## Adjusted R2 is: 0.06
Fitting linear model: N ~ gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 102.3 45.43 2.251 0.02751 *
gravel -105.4 52.41 -2.011 0.04814 *
sand -85.09 45.35 -1.876 0.0648
silt -80.89 54.21 -1.492 0.1402
Variance Inflation Factors
  gravel sand silt
VIF 2.01 5.13 5.16 
## RMSE for the full model: 28.13056 
## RMSE for the reduced model: 27.4413

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.13
Fitting linear model: H ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.323 0.977 -1.355 0.18
om 0.01376 0.1519 0.09059 0.9281
gravel 2.252 1.17 1.926 0.05827
sand 2.273 0.9846 2.308 0.02398 *
silt 3.306 1.306 2.531 0.01365 *
Variance Inflation Factors
  om gravel sand silt
VIF 1.83 2.15 5.34 5.96 
## REDUCED MODEL
## Adjusted R2 is: 0.15
Fitting linear model: H ~ gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.345 0.9417 -1.428 0.1578
gravel 2.29 1.086 2.108 0.03864 *
sand 2.297 0.9401 2.444 0.01706 *
silt 3.365 1.124 2.995 0.003795 * *
Variance Inflation Factors
  gravel sand silt
VIF 2.01 5.13 5.16 
## RMSE for the full model: 0.5407726 
## RMSE for the reduced model: 0.5336406

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.03
Fitting linear model: J ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1486 0.4941 -0.3007 0.7646
om -0.06691 0.07683 -0.8709 0.3868
gravel 1.28 0.5915 2.165 0.03389 *
sand 0.8545 0.4979 1.716 0.09064
silt 1.322 0.6606 2.001 0.04933 *
Variance Inflation Factors
  om gravel sand silt
VIF 1.83 2.15 5.34 5.96 
## REDUCED MODEL
## Adjusted R2 is: 0.04
Fitting linear model: J ~ gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.04525 0.4788 -0.09449 0.925
gravel 1.098 0.5524 1.988 0.05069
sand 0.7355 0.478 1.539 0.1284
silt 1.035 0.5714 1.811 0.07447
Variance Inflation Factors
  gravel sand silt
VIF 2.01 5.13 5.16 
## RMSE for the full model: 0.2730003 
## RMSE for the reduced model: 0.2724726

Design 2

Species richness
## FULL MODEL
## Adjusted R2 is: 0.21
Fitting linear model: S ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.579 2.427 3.536 0.002541 * *
arsenic -0.1901 0.2538 -0.7492 0.464
cadmium -13.98 22.35 -0.6253 0.5401
chromium -0.1582 0.1052 -1.504 0.1511
iron -3.955e-05 0.0001018 -0.3885 0.7025
mercury -62.83 40.07 -1.568 0.1353
lead 2.234 0.6924 3.227 0.00495 * *
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.1 1.78 3.63 2.74 1.21 3.22 
## REDUCED MODEL
## Adjusted R2 is: 0.27
Fitting linear model: S ~ chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.749 1.623 4.774 0.0001157 * * *
chromium -0.2192 0.07724 -2.838 0.01016 *
mercury -48.82 36.49 -1.338 0.1959
lead 1.996 0.6019 3.316 0.003444 * *
Variance Inflation Factors
  chromium mercury lead
VIF 2.77 1.14 2.91 
## RMSE for the full model: 3.363616 
## RMSE for the reduced model: 2.736833

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: N ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.33 16.58 2.131 0.04799 *
arsenic 0.3792 1.734 0.2187 0.8295
cadmium 22.2 152.7 0.1453 0.8861
chromium -0.795 0.719 -1.106 0.2843
iron -0.0005012 0.0006957 -0.7205 0.481
mercury -317.9 273.8 -1.161 0.2616
lead 9.857 4.731 2.084 0.05261
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.1 1.78 3.63 2.74 1.21 3.22 
## REDUCED MODEL
## Adjusted R2 is: 0.12
Fitting linear model: N ~ chromium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.3 10.34 2.446 0.02332 *
chromium -0.921 0.5068 -1.817 0.08346
lead 8.173 3.76 2.174 0.04133 *
Variance Inflation Factors
  chromium lead
VIF 2.7 2.7 
## RMSE for the full model: 27.13628 
## RMSE for the reduced model: 17.79233

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.07
Fitting linear model: H ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.403 0.4344 3.229 0.004935 * *
arsenic -0.04832 0.04543 -1.064 0.3023
cadmium -2.595 4.002 -0.6485 0.5253
chromium -0.02205 0.01884 -1.17 0.258
iron 3.069e-06 1.823e-05 0.1684 0.8683
mercury -5.478 7.173 -0.7637 0.4555
lead 0.3167 0.1239 2.555 0.02051 *
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.1 1.78 3.63 2.74 1.21 3.22 
## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: H ~ chromium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.366 0.2683 5.091 4.833e-05 * * *
chromium -0.0252 0.01315 -1.916 0.06901
lead 0.2401 0.09757 2.461 0.02261 *
Variance Inflation Factors
  chromium lead
VIF 2.7 2.7 
## RMSE for the full model: 0.5236763 
## RMSE for the reduced model: 0.5011726

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: -0.18
Fitting linear model: J ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5817 0.1962 2.964 0.008691 * *
arsenic -0.009212 0.02052 -0.4489 0.6592
cadmium 0.03793 1.808 0.02098 0.9835
chromium 0.006516 0.00851 0.7657 0.4544
iron 3.404e-06 8.234e-06 0.4134 0.6845
mercury 2.582 3.24 0.7968 0.4365
lead -0.05364 0.05599 -0.9579 0.3515
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.1 1.78 3.63 2.74 1.21 3.22 
## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7995 0.04169 19.17 1.21e-15 * * *

Quitting from lines 444-448 (C1_analyses_reg2.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 32 warnings (use warnings() to see them)

## RMSE for the full model: 0.3017948 
## RMSE for the reduced model: 0.2192119

5. Multivariate regressions

Independant variables are habitat parameters or heavy metal concentrations, dependant variables are species abundances. Outliers and correlated variables have been excluded from the analysis.

This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.

Design 1

Design 2

Elliot Dreujou

2019-12-28